In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
In [2]:
# DF1 = Total and facet scores, and score per question
df1 = pd.read_excel('Survey Data.xlsx', sheet_name = 'Risk Facet scores')
df1
Out[2]:
responseid q8_2_1 q8_2_2 q8_2_3 q8_2_4 q8_2_5 q8_3 q8_4 q8_5berlin_1 q8_5london_1 ... Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 1 1 1 1 2 2 4 1 1 ... 2 1 1 6 20 50 40 33.333333 25 31.578947
1 14129 2 2 2 1 2 4 3 1 1 ... 3 1 2 10 80 0 60 33.333333 50 52.631579
2 5861 1 1 1 2 2 4 4 2 1 ... 3 1 1 8 40 50 60 33.333333 25 42.105263
3 13364 2 1 2 1 1 4 1 2 1 ... 3 0 0 5 40 0 60 0.000000 0 26.315789
4 14112 2 2 2 2 2 2 3 2 1 ... 3 2 3 13 100 0 60 66.666667 75 68.421053
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
647 14135 1 1 2 1 2 4 3 2 1 ... 5 3 3 13 40 0 100 100.000000 75 68.421053
648 14155 2 2 2 2 2 4 4 2 1 ... 4 1 3 14 100 50 80 33.333333 75 73.684211
649 14274 1 1 1 2 2 3 4 2 1 ... 4 3 3 13 40 50 80 100.000000 75 68.421053
650 14448 2 2 2 2 2 2 2 2 1 ... 2 1 0 8 100 0 40 33.333333 0 42.105263
651 15717 1 2 1 1 2 4 4 2 1 ... 3 3 3 12 40 50 60 100.000000 75 63.157895

652 rows × 32 columns

In [3]:
# DF2 = Facet scores and total score, with Income, wealth and ISCED

df2 = pd.read_excel('Survey Data.xlsx', sheet_name = 'MAIN')
df2
Out[3]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 6 NaN 1 25 2 1 1 2 1 1 6 20 50 40 33.333333 25 31.578947
1 14129 1 3000.0 2 30 2 4 0 3 1 2 10 80 0 60 33.333333 50 52.631579
2 5861 2 0.0 1 43 3 2 1 3 1 1 8 40 50 60 33.333333 25 42.105263
3 13364 5 105000.0 1 32 2 2 0 3 0 0 5 40 0 60 0.000000 0 26.315789
4 14112 3 34865.0 2 32 2 5 0 3 2 3 13 100 0 60 66.666667 75 68.421053
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
647 14135 1 500.0 2 33 2 2 0 5 3 3 13 40 0 100 100.000000 75 68.421053
648 14155 6 NaN 2 29 2 5 1 4 1 3 14 100 50 80 33.333333 75 73.684211
649 14274 2 NaN 2 74 4 2 1 4 3 3 13 40 50 80 100.000000 75 68.421053
650 14448 3 NaN 2 31 2 5 0 2 1 0 8 100 0 40 33.333333 0 42.105263
651 15717 1 NaN 2 26 2 2 1 3 3 3 12 40 50 60 100.000000 75 63.157895

652 rows × 18 columns

In [4]:
# The following graph represents frequency of each data point on "Total Score_19" or the total score out of 19 questions across the sample.

df2['Total Score_19'].plot(kind = 'hist', xticks = np.arange(0, 20, step=1), xlabel = 'Total Score_19', title = 'Frequency of Scores' )
Out[4]:
<Axes: title={'center': 'Frequency of Scores'}, xlabel='Total Score_19', ylabel='Frequency'>
No description has been provided for this image
In [5]:
# Absolute mean scores for each facet

(df2[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4','Total Score_19']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')
Out[5]:
<Axes: title={'center': 'Absolute mean of scores for above data set'}>
No description has been provided for this image
In [6]:
# Normalised mean scores for each facet

df2[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean(axis = 0).plot(kind = 'bar', title = 'Normalised mean of scores for above data set')
Out[6]:
<Axes: title={'center': 'Normalised mean of scores for above data set'}>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [7]:
# DF3 = Sorted by income

df3 = df2.sort_values(by = 'Q12. Income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['Q12. Income'] == 7].index, inplace = True)

df3
Out[7]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 15717 1 NaN 2 26 2 2 1 3 3 3 12 40 50 60 100.000000 75 63.157895
1 219 1 80000.0 2 47 3 5 1 4 2 3 15 100 50 80 66.666667 75 78.947368
2 845 1 NaN 3 50 3 1 0 2 1 1 5 20 0 40 33.333333 25 26.315789
3 8599 1 2500.0 3 42 3 3 1 3 3 1 11 60 50 60 100.000000 25 57.894737
4 12715 1 200.0 2 31 2 3 0 2 0 2 7 60 0 40 0.000000 50 36.842105
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
572 7893 6 1000.0 1 41 3 1 1 3 1 4 10 20 50 60 33.333333 100 52.631579
573 6296 6 NaN 1 41 3 5 1 4 1 3 14 100 50 80 33.333333 75 73.684211
574 5806 6 NaN 1 40 3 2 1 3 2 3 11 40 50 60 66.666667 75 57.894737
575 1086 6 NaN 1 43 3 3 1 3 3 1 11 60 50 60 100.000000 25 57.894737
576 679 6 NaN 3 40 3 2 0 1 1 1 5 40 0 20 33.333333 25 26.315789

577 rows × 18 columns

In [8]:
# Absolute mean score for each facet (Sort by Income)

df3[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4','Total Score_19']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Income)')
Out[8]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [9]:
# Normalised mean score for each facet (Sort by Income)

(df3[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Income)')
Out[9]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [10]:
# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean()
Out[10]:
Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_%
Q12. Income
1 48.888889 38.333333 58.000000 60.740741 45.555556
2 57.217391 41.739130 63.304348 61.739130 50.869565
3 58.181818 42.045455 58.787879 63.131313 50.189394
4 62.886598 46.391753 63.711340 62.886598 55.154639
5 52.876712 36.301370 60.547945 57.990868 50.000000
6 64.857143 48.571429 69.142857 67.619048 59.642857
In [11]:
# Count of responses for each category (sorted by income)

df3.groupby('Q12. Income')[['responseid']].count()
Out[11]:
responseid
Q12. Income
1 90
2 115
3 132
4 97
5 73
6 70
In [12]:
# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('Q12. Income')[['Total Score_19']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[12]:
<matplotlib.legend.Legend at 0x18471d39b90>
No description has been provided for this image
In [13]:
# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[13]:
<matplotlib.legend.Legend at 0x18471ce8a50>
No description has been provided for this image
In [14]:
# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().plot( kind = 'line', title = 'Normalised mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[14]:
<matplotlib.legend.Legend at 0x1847213b110>
No description has been provided for this image
In [15]:
# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean().plot( kind = 'bar', title = 'Normalised mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[15]:
<matplotlib.legend.Legend at 0x184721013d0>
No description has been provided for this image
In [16]:
# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[16]:
<matplotlib.legend.Legend at 0x18471d18790>
No description has been provided for this image
In [17]:
# Trend line for Absolute Total Facet Score vs Q12. Income

sns.regplot (data = df3, x = 'Q12. Income', y = 'Total Score_19')
Out[17]:
<Axes: xlabel='Q12. Income', ylabel='Total Score_19'>
No description has been provided for this image
In [18]:
# Trend line for Absolute Independent Facet Score vs Q12. Income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax, label='Certainty score_5')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax, label='Uncertainty score_2')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax, label='Number Comprehension score_5')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax, label='Graph Comprehension score_3')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax, label='Calculation score_4' )

ax.set(ylabel='Scores', xlabel='Q12. Income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [19]:
# Violine Plot for Total Score_19 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Total Score_19')
Out[19]:
<Axes: xlabel='Q12. Income', ylabel='Total Score_19'>
No description has been provided for this image
In [20]:
# Violine Plot for Total Score_19 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Calculation score_4')
Out[20]:
<Axes: xlabel='Q12. Income', ylabel='Calculation score_4'>
No description has been provided for this image
In [21]:
# Violine Plot for Graph Comprehension score_3 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Graph Comprehension score_3')
Out[21]:
<Axes: xlabel='Q12. Income', ylabel='Graph Comprehension score_3'>
No description has been provided for this image
In [22]:
# Violine Plot for Number Comprehension score_5 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Number Comprehension score_5')
Out[22]:
<Axes: xlabel='Q12. Income', ylabel='Number Comprehension score_5'>
No description has been provided for this image
In [23]:
# Violine Plot for Uncertainty score_2 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Uncertainty score_2')
Out[23]:
<Axes: xlabel='Q12. Income', ylabel='Uncertainty score_2'>
No description has been provided for this image
In [24]:
# Violine Plot for Certainty score_5 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Certainty score_5')
Out[24]:
<Axes: xlabel='Q12. Income', ylabel='Certainty score_5'>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [25]:
# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'Q14. Wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'Q14. Wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["Q14. Wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4
Out[25]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
0 2305 4 0.0 1 29 2 1 0 3 2 2 8 20 0 60 66.666667 50 42.105263 1
1 5760 3 0.0 2 57 3 1 0 3 3 1 8 20 0 60 100.000000 25 42.105263 1
2 5463 6 0.0 1 30 2 2 1 4 3 2 12 40 50 80 100.000000 50 63.157895 1
3 796 2 0.0 1 35 2 5 2 4 2 3 16 100 100 80 66.666667 75 84.210526 1
4 2578 3 0.0 3 33 2 1 0 2 2 0 5 20 0 40 66.666667 0 26.315789 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
375 15 6 1250000.0 2 68 4 2 1 4 3 3 13 40 50 80 100.000000 75 68.421053 4
376 970 6 1350000.0 3 64 4 2 1 4 3 4 14 40 50 80 100.000000 100 73.684211 4
377 5592 7 1500000.0 2 64 4 3 1 5 3 3 15 60 50 100 100.000000 75 78.947368 4
378 10809 6 1650000.0 1 59 3 3 2 5 3 2 15 60 100 100 100.000000 50 78.947368 4
379 278 6 3500000.0 1 66 4 5 2 3 3 4 17 100 100 60 100.000000 100 89.473684 4

380 rows × 19 columns

In [26]:
# Absolute mean score for each facet (Sort by Wealth)

df4[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4','Total Score_19']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Wealth)')
Out[26]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [27]:
# Normalised mean score for each facet (Sort by Wealth)

(df4[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Wealth)')
Out[27]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [28]:
# Line Plot of Absolute Total Facet score vs sorted wealth

df4.plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total responses line plot sorted by Wealth',xlabel = 'Unique responses sorted by wealth', ylabel = 'Total Score_19')
Out[28]:
<Axes: title={'center': 'Total responses line plot sorted by Wealth'}, xlabel='Unique responses sorted by wealth', ylabel='Total Score_19'>
No description has been provided for this image
In [29]:
# Absolute Facet scores line plot from low to high wealth 
# I do understand that this is non readable

df4[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].plot( kind = 'line', title = 'Absolute Facet scores line plot', use_index = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[29]:
<matplotlib.legend.Legend at 0x18475815d50>
No description has been provided for this image
In [30]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'Total Score_19')
Out[30]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [31]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax1, label='Certainty score_5')
sns.regplot (data = df4, x = df4.index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax1, label='Uncertainty score_2')
sns.regplot (data = df4, x = df4.index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax1, label='Number Comprehension score_5')
sns.regplot (data = df4, x = df4.index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax1, label='Graph Comprehension score_3')
sns.regplot (data = df4, x = df4.index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax1, label='Calculation score_4' )

ax1.set(ylabel='Scores', xlabel='Q14. Wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [32]:
# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()
Out[32]:
Quartile Number
Quartile Number
1 122
2 74
3 89
4 95
In [33]:
df4.loc[df4['Quartile Number']==1]
Out[33]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
0 2305 4 0.0 1 29 2 1 0 3 2 2 8 20 0 60 66.666667 50 42.105263 1
1 5760 3 0.0 2 57 3 1 0 3 3 1 8 20 0 60 100.000000 25 42.105263 1
2 5463 6 0.0 1 30 2 2 1 4 3 2 12 40 50 80 100.000000 50 63.157895 1
3 796 2 0.0 1 35 2 5 2 4 2 3 16 100 100 80 66.666667 75 84.210526 1
4 2578 3 0.0 3 33 2 1 0 2 2 0 5 20 0 40 66.666667 0 26.315789 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
117 8449 2 5000.0 3 38 3 5 0 2 3 2 12 100 0 40 100.000000 50 63.157895 1
118 8979 2 5000.0 3 26 2 4 1 3 1 1 10 80 50 60 33.333333 25 52.631579 1
119 10476 4 5000.0 1 23 2 0 0 3 2 2 7 0 0 60 66.666667 50 36.842105 1
120 9214 3 5000.0 2 35 2 3 0 4 0 3 10 60 0 80 0.000000 75 52.631579 1
121 1952 1 5000.0 3 61 4 1 2 3 2 2 10 20 100 60 66.666667 50 52.631579 1

122 rows × 19 columns

In [34]:
# This graph is basically a line plot of the Total score_19 across first quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==1].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[34]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [35]:
# Trend line for Absolute Total Facet Score vs unique Q14. Income reponses in Quartile 1

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Total Score_19')
Out[35]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [36]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 1

fig1, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax2, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax2, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax2, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax2, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax2, label='Calculation score_4' )

ax2.set(ylabel='Scores', xlabel='Q14. Wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [37]:
df4.loc[df4['Quartile Number']==2]
Out[37]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
122 2053 2 6000.0 3 23 2 5 0 0 2 2 9 100 0 0 66.666667 50 47.368421 2
123 1326 1 6200.0 1 49 3 4 1 3 2 1 11 80 50 60 66.666667 25 57.894737 2
124 10822 5 6666.0 2 28 2 3 0 3 0 0 6 60 0 60 0.000000 0 31.578947 2
125 6128 1 8000.0 1 25 2 5 2 4 2 3 16 100 100 80 66.666667 75 84.210526 2
126 13429 2 8000.0 2 24 2 5 0 3 2 2 12 100 0 60 66.666667 50 63.157895 2
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
191 11387 6 30000.0 2 37 3 3 0 4 2 3 12 60 0 80 66.666667 75 63.157895 2
192 13397 3 30000.0 1 28 2 1 1 4 2 2 10 20 50 80 66.666667 50 52.631579 2
193 6789 4 30000.0 1 52 3 3 2 4 2 3 14 60 100 80 66.666667 75 73.684211 2
194 391 2 30000.0 3 52 3 2 1 0 1 3 7 40 50 0 33.333333 75 36.842105 2
195 10277 6 30000.0 1 31 2 3 2 4 3 2 14 60 100 80 100.000000 50 73.684211 2

74 rows × 19 columns

In [38]:
# This graph is basically a line plot of the Total score_19 across second quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==2].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[38]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [39]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses in Quartile 2

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Total Score_19')
Out[39]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [40]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 2

fig1, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax3, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax3, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax3, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax3, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax3, label='Calculation score_4' )

ax3.set(ylabel='Scores', xlabel='Q14. Wealth')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [41]:
df4.loc[df4['Quartile Number']==3]
Out[41]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
196 5909 3 32500.0 2 30 2 5 0 1 1 3 10 100 0 20 33.333333 75 52.631579 3
197 10855 4 34000.0 2 55 3 2 0 2 1 2 7 40 0 40 33.333333 50 36.842105 3
198 14112 3 34865.0 2 32 2 5 0 3 2 3 13 100 0 60 66.666667 75 68.421053 3
199 13314 5 35000.0 2 30 2 3 0 1 3 0 7 60 0 20 100.000000 0 36.842105 3
200 10801 3 35000.0 2 52 3 1 2 3 3 2 11 20 100 60 100.000000 50 57.894737 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
280 9549 4 160000.0 2 42 3 5 0 4 3 3 15 100 0 80 100.000000 75 78.947368 3
281 9194 3 160000.0 2 43 3 3 1 2 2 1 9 60 50 40 66.666667 25 47.368421 3
282 1013 5 175000.0 3 73 4 4 1 4 2 2 13 80 50 80 66.666667 50 68.421053 3
283 8166 6 175000.0 2 60 4 3 1 4 3 3 14 60 50 80 100.000000 75 73.684211 3
284 5837 4 180000.0 2 54 3 5 1 4 2 3 15 100 50 80 66.666667 75 78.947368 3

89 rows × 19 columns

In [42]:
# This graph is basically a line plot of the Total score_19 across third quartile unique responses which are ordered by wealth

df4.loc[df4['Quartile Number']==3].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q3',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[42]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q3'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [43]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses in Quartile 3

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Total Score_19')
Out[43]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [44]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 3

fig1, ax4 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax4, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax4, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax4, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax4, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax4, label='Calculation score_4' )

ax4.set(ylabel='Scores', xlabel='Q14. Wealth')
ax4.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [45]:
df4.loc[df4['Quartile Number']==4]
Out[45]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
285 11536 4 185000.0 2 51 3 2 1 2 0 1 6 40 50 40 0.000000 25 31.578947 4
286 1315 2 200000.0 2 57 3 1 2 4 2 3 12 20 100 80 66.666667 75 63.157895 4
287 490 1 200000.0 3 65 4 1 1 4 3 1 10 20 50 80 100.000000 25 52.631579 4
288 13403 5 200000.0 2 32 2 3 1 3 2 2 11 60 50 60 66.666667 50 57.894737 4
289 13350 6 200000.0 1 30 2 2 2 3 2 2 11 40 100 60 66.666667 50 57.894737 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
375 15 6 1250000.0 2 68 4 2 1 4 3 3 13 40 50 80 100.000000 75 68.421053 4
376 970 6 1350000.0 3 64 4 2 1 4 3 4 14 40 50 80 100.000000 100 73.684211 4
377 5592 7 1500000.0 2 64 4 3 1 5 3 3 15 60 50 100 100.000000 75 78.947368 4
378 10809 6 1650000.0 1 59 3 3 2 5 3 2 15 60 100 100 100.000000 50 78.947368 4
379 278 6 3500000.0 1 66 4 5 2 3 3 4 17 100 100 60 100.000000 100 89.473684 4

95 rows × 19 columns

In [46]:
# This graph is basically a line plot of the Total score_19 across fourth quartile unique responses which are ordered by Wealth

df4.loc[df4['Quartile Number']==4].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q4',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[46]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q4'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [47]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses in Quartile 4

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Total Score_19')
Out[47]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [48]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 4

fig1, ax5 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax5, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax5, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax5, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax5, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax5, label='Calculation score_4' )

ax5.set(ylabel='Scores', xlabel='Q14. Wealth')
ax5.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [49]:
# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['Total Score_19']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[49]:
<matplotlib.legend.Legend at 0x184779c8350>
No description has been provided for this image
In [50]:
# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[50]:
<matplotlib.legend.Legend at 0x18477dc8fd0>
No description has been provided for this image
In [51]:
# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[51]:
<matplotlib.legend.Legend at 0x184755ebf90>
No description has been provided for this image
In [52]:
# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[52]:
<matplotlib.legend.Legend at 0x184780ff6d0>
No description has been provided for this image
In [53]:
# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[53]:
<matplotlib.legend.Legend at 0x18477a3dc90>
No description has been provided for this image
In [54]:
# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'Total Score_19')
Out[54]:
<Axes: xlabel='Quartile Number', ylabel='Total Score_19'>
No description has been provided for this image
In [55]:
# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax2, label='Certainty score_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax2, label='Uncertainty score_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax2, label='Number Comprehension score_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax2, label='Graph Comprehension score_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax2, label='Calculation score_4' )

ax2.set(ylabel='Scores', xlabel='Q14. Wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [56]:
# Violine Plot for Total Score_19 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Total Score_19')
Out[56]:
<Axes: xlabel='Quartile Number', ylabel='Total Score_19'>
No description has been provided for this image
In [57]:
# Violine Plot for Calculation score_4 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Calculation score_4')
Out[57]:
<Axes: xlabel='Quartile Number', ylabel='Calculation score_4'>
No description has been provided for this image
In [58]:
# Violine Plot for Graph Comprehension score_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Graph Comprehension score_3')
Out[58]:
<Axes: xlabel='Quartile Number', ylabel='Graph Comprehension score_3'>
No description has been provided for this image
In [59]:
# Violine Plot for Number Comprehension score_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Number Comprehension score_5')
Out[59]:
<Axes: xlabel='Quartile Number', ylabel='Number Comprehension score_5'>
No description has been provided for this image
In [60]:
# Violine Plot for Certainty score_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty score_5')
Out[60]:
<Axes: xlabel='Quartile Number', ylabel='Certainty score_5'>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [61]:
# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5
Out[61]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 6 NaN 1 25 2 1 1 2 1 1 6 20 50 40 33.333333 25 31.578947
1 7875 5 NaN 1 63 4 1 1 3 2 2 9 20 50 60 66.666667 50 47.368421
2 7876 6 25000.0 1 41 3 3 2 4 3 3 15 60 100 80 100.000000 75 78.947368
3 7877 7 NaN 1 55 3 5 2 4 3 3 17 100 100 80 100.000000 75 89.473684
4 7884 6 950000.0 1 52 3 5 2 5 2 4 18 100 100 100 66.666667 100 94.736842
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
647 78 1 NaN 3 51 3 2 2 5 3 2 14 40 100 100 100.000000 50 73.684211
648 6130 1 NaN 3 32 2 2 0 2 1 0 5 40 0 40 33.333333 0 26.315789
649 6138 7 NaN 3 34 2 2 1 3 0 0 6 40 50 60 0.000000 0 31.578947
650 467 7 NaN 3 55 3 4 0 3 2 3 12 80 0 60 66.666667 75 63.157895
651 405 2 NaN 3 48 3 5 0 4 1 2 12 100 0 80 33.333333 50 63.157895

652 rows × 18 columns

In [62]:
# since we didnt drop any values, the dataset is essentially the same as df2, but sorted
In [63]:
# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean()
Out[63]:
Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_%
isced
1 60.000000 47.727273 66.136364 66.035354 57.670455
2 55.462185 36.134454 60.000000 60.224090 46.008403
3 53.466667 41.333333 57.466667 58.222222 46.166667
In [64]:
# Count of responses for each category isced

df5.groupby('isced')[['responseid']].count()
Out[64]:
responseid
isced
1 264
2 238
3 150
In [65]:
# Absolute Total Facet scores mean line plot from low to high education

df5.groupby('isced')[['Total Score_19']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[65]:
<matplotlib.legend.Legend at 0x184775accd0>
No description has been provided for this image
In [66]:
# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[66]:
<matplotlib.legend.Legend at 0x18479eaac50>
No description has been provided for this image
In [67]:
# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[67]:
<matplotlib.legend.Legend at 0x1847841e490>
No description has been provided for this image
In [68]:
# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[68]:
<matplotlib.legend.Legend at 0x18477e74c90>
No description has been provided for this image
In [69]:
# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[69]:
<matplotlib.legend.Legend at 0x184775e2590>
No description has been provided for this image
In [70]:
# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'Total Score_19')
Out[70]:
<Axes: xlabel='isced', ylabel='Total Score_19'>
No description has been provided for this image
In [71]:
# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax6, label='Certainty score_5')
sns.regplot (data = df5, x = 'isced', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax6, label='Uncertainty score_2')
sns.regplot (data = df5, x = 'isced', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax6, label='Number Comprehension score_5')
sns.regplot (data = df5, x = 'isced', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax6, label='Graph Comprehension score_3')
sns.regplot (data = df5, x = 'isced', y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax6, label='Calculation score_4' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [72]:
# Violine Plot for Total Score_19 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Total Score_19')
Out[72]:
<Axes: xlabel='isced', ylabel='Total Score_19'>
No description has been provided for this image
In [73]:
# Violine Plot for Total Score_19 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Calculation score_4')
Out[73]:
<Axes: xlabel='isced', ylabel='Calculation score_4'>
No description has been provided for this image
In [74]:
# Violine Plot for Graph Comprehension score_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Graph Comprehension score_3')
Out[74]:
<Axes: xlabel='isced', ylabel='Graph Comprehension score_3'>
No description has been provided for this image
In [75]:
# Violine Plot for Number Comprehension score_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Number Comprehension score_5')
Out[75]:
<Axes: xlabel='isced', ylabel='Number Comprehension score_5'>
No description has been provided for this image
In [76]:
# Violine Plot for Uncertainty score_2 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Uncertainty score_2')
Out[76]:
<Axes: xlabel='isced', ylabel='Uncertainty score_2'>
No description has been provided for this image
In [77]:
# Violine Plot for Certainty score_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty score_5')
Out[77]:
<Axes: xlabel='isced', ylabel='Certainty score_5'>
No description has been provided for this image
In [78]:
# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'Q14. Wealth')
df6.drop(df6[df6['Q14. Wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['Q14. Wealth']].describe()

C:\Users\SHIVAM\AppData\Local\Temp\ipykernel_5736\3962586432.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df6.drop(df6[df6['Q14. Wealth'] == 3500000].index, inplace = True)
Out[78]:
Q14. Wealth
count 1.690000e+02
mean 1.708374e+05
std 2.564941e+05
min 0.000000e+00
25% 5.000000e+03
50% 5.500000e+04
75% 2.500000e+05
max 1.650000e+06
In [79]:
# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['Q14. Wealth']].describe()
Out[79]:
Q14. Wealth
count 1.360000e+02
mean 1.387606e+05
std 2.632906e+05
min 0.000000e+00
25% 5.000000e+03
50% 3.000000e+04
75% 1.600000e+05
max 1.500000e+06
In [80]:
# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['Q14. Wealth']].describe()
Out[80]:
Q14. Wealth
count 7.400000e+01
mean 9.383407e+04
std 2.127450e+05
min 0.000000e+00
25% 1.000000e+03
50% 1.000000e+04
75% 4.875000e+04
max 1.350000e+06
In [81]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [82]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [83]:
# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty score_5', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [84]:
# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='Uncertainty score_2', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [85]:
# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Number Comprehension score_5', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [86]:
# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Graph Comprehension score_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [87]:
# Trend line for Absolute Calculation Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Calculation score_4', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [88]:
# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['Q12. Income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1].describe()
Out[88]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 240.000000 240.000000 1.660000e+02 240.0 240.000000 240.000000 240.000000 240.000000 240.0000 240.000000 240.000000 240.000000 240.000000 240.000000 240.000000 240.000000 240.000000 240.000000
mean 7076.962500 3.704167 1.901598e+05 1.0 44.220833 2.833333 3.016667 0.950000 3.3125 1.979167 2.325000 11.583333 60.333333 47.500000 66.250000 65.972222 58.125000 60.964912
std 4216.886542 1.628712 3.650422e+05 0.0 15.257524 0.763534 1.563085 0.730106 1.0969 0.978633 1.079768 3.654729 31.261706 36.505287 21.938007 32.621110 26.994207 19.235415
min 1.000000 1.000000 0.000000e+00 1.0 18.000000 2.000000 0.000000 0.000000 0.0000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10.526316
25% 5463.750000 2.000000 5.000000e+03 1.0 31.000000 2.000000 2.000000 0.000000 3.0000 1.000000 2.000000 9.000000 40.000000 0.000000 60.000000 33.333333 50.000000 47.368421
50% 7312.000000 4.000000 5.250000e+04 1.0 41.000000 3.000000 3.000000 1.000000 4.0000 2.000000 2.000000 12.000000 60.000000 50.000000 80.000000 66.666667 50.000000 63.157895
75% 10478.500000 5.000000 2.500000e+05 1.0 59.000000 3.000000 5.000000 1.000000 4.0000 3.000000 3.000000 14.000000 100.000000 50.000000 80.000000 100.000000 75.000000 73.684211
max 13542.000000 6.000000 3.500000e+06 1.0 75.000000 4.000000 5.000000 2.000000 5.0000 3.000000 4.000000 18.000000 100.000000 100.000000 100.000000 100.000000 100.000000 94.736842
In [89]:
df7.loc[df7['isced']==2].describe()
Out[89]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 211.000000 211.000000 1.330000e+02 211.0 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000 211.000000
mean 7491.421801 3.175355 1.305748e+05 2.0 44.516588 2.810427 2.843602 0.725118 2.962085 1.791469 1.834123 10.156398 56.872038 36.255924 59.241706 59.715640 45.853081 53.454727
std 4805.917560 1.398148 2.377201e+05 0.0 14.310950 0.705141 1.482981 0.676324 1.154075 1.006703 1.123757 3.296346 29.659620 33.816182 23.081499 33.556763 28.093928 17.349191
min 15.000000 1.000000 0.000000e+00 2.0 18.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10.526316
25% 2308.500000 2.000000 5.000000e+03 2.0 31.500000 2.000000 2.000000 0.000000 2.000000 1.000000 1.000000 8.000000 40.000000 0.000000 40.000000 33.333333 25.000000 42.105263
50% 8019.000000 3.000000 3.000000e+04 2.0 44.000000 3.000000 3.000000 1.000000 3.000000 2.000000 2.000000 10.000000 60.000000 50.000000 60.000000 66.666667 50.000000 52.631579
75% 11703.000000 4.000000 1.600000e+05 2.0 56.000000 3.000000 4.000000 1.000000 4.000000 3.000000 3.000000 13.000000 80.000000 50.000000 80.000000 100.000000 75.000000 68.421053
max 15717.000000 6.000000 1.250000e+06 2.0 74.000000 4.000000 5.000000 2.000000 5.000000 3.000000 4.000000 17.000000 100.000000 100.000000 100.000000 100.000000 100.000000 89.473684
In [90]:
df7.loc[df7['isced']==3].describe()
Out[90]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 126.000000 126.000000 7.400000e+01 126.0 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000
mean 1647.952381 2.619048 9.383407e+04 3.0 51.817460 3.158730 2.650794 0.841270 2.896825 1.793651 1.944444 10.126984 53.015873 42.063492 57.936508 59.788360 48.611111 53.299916
std 2500.040370 1.563878 2.127450e+05 0.0 13.992799 0.742026 1.493010 0.598835 1.313495 0.940787 1.119325 3.445540 29.860203 29.941742 26.269906 31.359552 27.983130 18.134421
min 12.000000 1.000000 0.000000e+00 3.0 20.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.263158
25% 493.250000 1.000000 1.000000e+03 3.0 43.250000 3.000000 1.000000 0.000000 2.000000 1.000000 1.000000 8.000000 20.000000 0.000000 40.000000 33.333333 25.000000 42.105263
50% 786.000000 2.000000 1.000000e+04 3.0 51.500000 3.000000 2.000000 1.000000 3.000000 2.000000 2.000000 10.000000 40.000000 50.000000 60.000000 66.666667 50.000000 52.631579
75% 1268.500000 3.000000 4.875000e+04 3.0 63.000000 4.000000 4.000000 1.000000 4.000000 3.000000 3.000000 13.000000 80.000000 50.000000 80.000000 100.000000 75.000000 68.421053
max 10820.000000 6.000000 1.350000e+06 3.0 75.000000 4.000000 5.000000 2.000000 5.000000 3.000000 4.000000 17.000000 100.000000 100.000000 100.000000 100.000000 100.000000 89.473684
In [91]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[df7['isced']==1]['Total Score_19'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[df7['isced']==2]['Total Score_19'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[df7['isced']==3]['Total Score_19'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [92]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [93]:
# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty score_5', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [94]:
# Trend line for Absolute Uncertainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='Uncertainty score_2', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [95]:
# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Number Comprehension score_5', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [96]:
# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Graph Comprehension score_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [97]:
# Trend line for Absolute Calculation Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Calculation score_4', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [98]:
df7
Out[98]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 6 NaN 1 25 2 1 1 2 1 1 6 20 50 40 33.333333 25 31.578947
1 7875 5 NaN 1 63 4 1 1 3 2 2 9 20 50 60 66.666667 50 47.368421
2 7876 6 25000.0 1 41 3 3 2 4 3 3 15 60 100 80 100.000000 75 78.947368
4 7884 6 950000.0 1 52 3 5 2 5 2 4 18 100 100 100 66.666667 100 94.736842
5 8688 3 155000.0 1 50 3 4 0 4 1 1 10 80 0 80 33.333333 25 52.631579
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
645 115 2 100000.0 3 49 3 2 1 3 1 2 9 40 50 60 33.333333 50 47.368421
646 104 1 1000.0 3 67 4 1 0 3 0 1 5 20 0 60 0.000000 25 26.315789
647 78 1 NaN 3 51 3 2 2 5 3 2 14 40 100 100 100.000000 50 73.684211
648 6130 1 NaN 3 32 2 2 0 2 1 0 5 40 0 40 33.333333 0 26.315789
651 405 2 NaN 3 48 3 5 0 4 1 2 12 100 0 80 33.333333 50 63.157895

577 rows × 18 columns

In [99]:
# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)
In [100]:
df7.loc[(df7['age_rec']==2)].describe()
Out[100]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 195.000000 195.000000 122.000000 195.000000 195.000000 195.0 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000
mean 9526.461538 3.164103 47579.500000 1.656410 29.225641 2.0 2.800000 0.620513 2.774359 1.558974 1.841026 9.594872 56.000000 31.025641 55.487179 51.965812 46.025641 50.499325
std 4751.872248 1.503859 104410.492785 0.703377 4.042348 0.0 1.648492 0.688334 1.148894 1.015528 1.153372 3.525310 32.969839 34.416706 22.977882 33.850946 28.834300 18.554262
min 263.000000 1.000000 0.000000 1.000000 18.000000 2.0 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.263158
25% 5810.500000 2.000000 1000.000000 1.000000 26.000000 2.0 1.000000 0.000000 2.000000 1.000000 1.000000 7.000000 20.000000 0.000000 40.000000 33.333333 25.000000 36.842105
50% 11870.000000 3.000000 5500.000000 2.000000 30.000000 2.0 3.000000 1.000000 3.000000 2.000000 2.000000 10.000000 60.000000 50.000000 60.000000 66.666667 50.000000 52.631579
75% 13450.000000 4.000000 50000.000000 2.000000 32.000000 2.0 4.500000 1.000000 4.000000 2.000000 3.000000 12.000000 90.000000 50.000000 80.000000 66.666667 75.000000 63.157895
max 15717.000000 6.000000 800000.000000 3.000000 35.000000 2.0 5.000000 2.000000 5.000000 3.000000 4.000000 16.000000 100.000000 100.000000 100.000000 100.000000 100.000000 84.210526
In [101]:
df7.loc[(df7['age_rec']==3)].describe()
Out[101]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 247.000000 247.000000 1.520000e+02 247.000000 247.000000 247.0 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000
mean 4563.182186 3.578947 1.374157e+05 1.838057 48.105263 3.0 3.072874 0.898785 3.190283 2.020243 2.105263 11.287449 61.457490 44.939271 63.805668 67.341430 52.631579 59.407628
std 3863.130621 1.650586 2.406952e+05 0.758481 6.781510 0.0 1.468765 0.693962 1.137063 0.934651 1.073069 3.382518 29.375291 34.698123 22.741269 31.155030 26.826715 17.802726
min 1.000000 1.000000 0.000000e+00 1.000000 36.000000 3.0 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10.526316
25% 730.000000 2.000000 5.000000e+03 1.000000 42.000000 3.0 2.000000 0.000000 2.000000 1.000000 1.000000 9.000000 40.000000 0.000000 40.000000 33.333333 25.000000 47.368421
50% 5483.000000 3.000000 3.450000e+04 2.000000 48.000000 3.0 3.000000 1.000000 3.000000 2.000000 2.000000 11.000000 60.000000 50.000000 60.000000 66.666667 50.000000 57.894737
75% 7937.500000 5.000000 1.812500e+05 2.000000 54.000000 3.0 5.000000 1.000000 4.000000 3.000000 3.000000 14.000000 100.000000 50.000000 80.000000 100.000000 75.000000 73.684211
max 11536.000000 6.000000 1.650000e+06 3.000000 59.000000 3.0 5.000000 2.000000 5.000000 3.000000 4.000000 18.000000 100.000000 100.000000 100.000000 100.000000 100.000000 94.736842
In [102]:
df7.loc[(df7['age_rec']==4)].describe()
Out[102]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 135.000000 135.000000 9.900000e+01 135.000000 135.000000 135.0 135.000000 135.000000 135.000000 135.000000 135.000000 135.000000 135.000000 135.000000 135.000000 135.000000 135.000000 135.000000
mean 3718.792593 2.874074 2.947959e+05 1.948148 66.325926 4.0 2.614815 1.066667 3.377778 2.044444 2.303704 11.407407 52.296296 53.333333 67.555556 68.148148 57.592593 60.038986
std 3146.240925 1.483389 4.501179e+05 0.857961 4.221179 0.0 1.387498 0.588446 1.208593 0.921280 1.128161 3.490491 27.749969 29.422298 24.171856 30.709322 28.204026 18.371005
min 15.000000 1.000000 0.000000e+00 1.000000 60.000000 4.0 1.000000 0.000000 0.000000 0.000000 0.000000 2.000000 20.000000 0.000000 0.000000 0.000000 0.000000 10.526316
25% 695.000000 2.000000 1.850000e+04 1.000000 62.500000 4.0 1.500000 1.000000 3.000000 1.500000 1.500000 9.000000 30.000000 50.000000 60.000000 50.000000 37.500000 47.368421
50% 2307.000000 3.000000 1.000000e+05 2.000000 67.000000 4.0 2.000000 1.000000 4.000000 2.000000 2.000000 12.000000 40.000000 50.000000 80.000000 66.666667 50.000000 63.157895
75% 6230.000000 4.000000 4.850000e+05 3.000000 69.000000 4.0 3.000000 1.000000 4.000000 3.000000 3.000000 14.000000 60.000000 50.000000 80.000000 100.000000 75.000000 73.684211
max 14274.000000 6.000000 3.500000e+06 3.000000 75.000000 4.0 5.000000 2.000000 5.000000 3.000000 4.000000 18.000000 100.000000 100.000000 100.000000 100.000000 100.000000 94.736842
In [ ]:
In [103]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_19', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [104]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_19', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [105]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty score_5', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty score_5', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty score_5', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [106]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Uncertainty score_2', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Uncertainty score_2', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Uncertainty score_2', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [107]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Number Comprehension score_5', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Number Comprehension score_5', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Number Comprehension score_5', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [108]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Graph Comprehension score_3', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Graph Comprehension score_3', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Graph Comprehension score_3', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [109]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Calculation score_4', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Calculation score_4', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Calculation score_4', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [110]:
# Age median method
# Age_median
age_m = df7['age'].mean()
age_m
Out[110]:
45.98786828422877
In [111]:
df7.loc[(df7['age'] < age_m)].describe()
Out[111]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 281.000000 281.000000 169.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000 281.000000
mean 8334.572954 3.419929 50636.384615 1.637011 32.637011 2.306050 2.992883 0.725979 2.914591 1.690391 1.985765 10.309609 59.857651 36.298932 58.291815 56.346382 49.644128 54.261098
std 4799.254754 1.572587 102262.480828 0.694301 6.344563 0.461673 1.621271 0.701784 1.167950 1.014298 1.133804 3.624354 32.425426 35.089217 23.359005 33.809930 28.345094 19.075548
min 1.000000 1.000000 0.000000 1.000000 18.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.263158
25% 3171.000000 2.000000 1000.000000 1.000000 29.000000 2.000000 2.000000 0.000000 2.000000 1.000000 1.000000 8.000000 40.000000 0.000000 40.000000 33.333333 25.000000 42.105263
50% 8669.000000 3.000000 8000.000000 2.000000 32.000000 2.000000 3.000000 1.000000 3.000000 2.000000 2.000000 10.000000 60.000000 50.000000 60.000000 66.666667 50.000000 52.631579
75% 13360.000000 5.000000 50000.000000 2.000000 37.000000 3.000000 5.000000 1.000000 4.000000 3.000000 3.000000 13.000000 100.000000 50.000000 80.000000 100.000000 75.000000 68.421053
max 15717.000000 6.000000 800000.000000 3.000000 45.000000 3.000000 5.000000 2.000000 5.000000 3.000000 4.000000 18.000000 100.000000 100.000000 100.000000 100.000000 100.000000 94.736842
In [112]:
df7.loc[(df7['age'] > age_m)].describe()
Out[112]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
count 296.000000 296.000000 2.040000e+02 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000 296.000000
mean 3867.527027 3.135135 2.319566e+05 1.959459 58.662162 3.456081 2.760135 0.956081 3.263514 2.040541 2.135135 11.155405 55.202703 47.804054 65.270270 68.018018 53.378378 58.712660
std 3536.237036 1.590858 3.747336e+05 0.809925 8.172342 0.498911 1.416572 0.659813 1.169450 0.923357 1.114762 3.428125 28.331443 32.990632 23.389001 30.778567 27.869041 18.042761
min 12.000000 1.000000 0.000000e+00 1.000000 46.000000 3.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10.526316
25% 574.250000 2.000000 1.425000e+04 1.000000 51.000000 3.000000 2.000000 1.000000 2.750000 1.000000 1.000000 9.000000 40.000000 50.000000 55.000000 33.333333 25.000000 47.368421
50% 2231.500000 3.000000 8.000000e+04 2.000000 58.500000 3.000000 3.000000 1.000000 4.000000 2.000000 2.000000 11.000000 60.000000 50.000000 80.000000 66.666667 50.000000 57.894737
75% 6745.750000 4.000000 3.425000e+05 3.000000 66.000000 4.000000 4.000000 1.000000 4.000000 3.000000 3.000000 14.000000 80.000000 50.000000 80.000000 100.000000 75.000000 73.684211
max 14274.000000 6.000000 3.500000e+06 3.000000 75.000000 4.000000 5.000000 2.000000 5.000000 3.000000 4.000000 18.000000 100.000000 100.000000 100.000000 100.000000 100.000000 94.736842
In [ ]:
In [113]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [114]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [115]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty score_5', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty score_5', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [116]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Uncertainty score_2', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Uncertainty score_2', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [117]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Number Comprehension score_5', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Number Comprehension score_5', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [118]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Graph Comprehension score_3', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Graph Comprehension score_3', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [119]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Calculation score_4', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Calculation score_4', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [120]:
# 3D graph representation

fig = plt.figure(figsize = (6,6))
 
ax = fig.add_subplot(111, projection='3d')
 
# defining axes
z = df7.loc[:,'age'] # Age on Z axis
x = df7.loc[:,'Q12. Income'] # Income on X axis
y = df7.loc[:,'isced'] # ISCED on Y axis
ax.scatter(x, y, z)
 
ax.set_title('3d Scatter plot')
ax.set(ylabel='ISCED', xlabel='Income', zlabel = 'Age')

plt.show()
No description has been provided for this image
In [121]:
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot(111, projection='3d')

z = df7.loc[:,'age'] # Age on Z axis
x = df7.loc[:,'Q12. Income'] # Income on X axis
y = df7.loc[:,'isced'] # ISCED on Y axis
c = df7.loc[:,'Total Score_19'] # on Heat scale

img = ax.scatter(x, y, z, c=c, cmap=plt.hot())
ax.set(ylabel='ISCED', xlabel='Income', zlabel = 'Age')
ax.set_title('3d Scatter plot')

fig.colorbar(img, anchor = (2.0,2.0), label = 'Total Score_19')
plt.show()
No description has been provided for this image
In [122]:
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot(111, projection='3d')

c = df7.loc[:,'age'] # Age on Heat scale
x = df7.loc[:,'Q12. Income'] # Income on X axis
z = df7.loc[:,'isced'] # ISCED on Z axis
y = df7.loc[:,'Total Score_19'] # on Y axis

img = ax.scatter(x, y, z, c=c, cmap=plt.hot())
ax.set(ylabel='Total Score_19', xlabel='Income', zlabel = 'ISCED')
ax.set_title('3d Scatter plot')

fig.colorbar(img, anchor = (2.0,2.0), label = 'Age')
plt.show()
No description has been provided for this image
In [123]:
# 3D plots are amounting to be nigh unreadable, and usually speaking, the methods that we have used above are far more easier to visualise.
# However, I have included them just in case you are able to interpret them in your expertise.
In [ ]:
In [ ]:
In [ ]: